Posterior-stabilized total knee prostheses have been widely used in orthopedic clinical treatment of knee osteoarthritis, but the patients and surgeons are still troubled by the complications, for example severe wear and fracture of the post, as well as prosthetic loosening. Understanding the in vivo biomechanics of knee prostheses will aid in the decrease of postoperative prosthetic revision and patient dissatisfaction. Therefore, six different designs of posterior-stabilized total knee prostheses were used to establish the musculoskeletal multibody dynamics models of total knee arthroplasty respectively, and the biomechanical differences of six posterior-stabilized total knee prostheses were investigated under three simulated physiological activities: walking, right turn and squatting. The results showed that the post contact forces of PFC Sigma and Scorpio NGR prostheses were larger during walking, turning right, and squatting, which may increase the risk of the fracture and wear as well as the early loosening. The post design of Gemini SL prosthesis was more conductive to the knee internal-external rotation and avoided the edge contact and wear. The lower conformity design in sagittal plane and the later post-cam engagement resulted in the larger anterior-posterior translation. This study provides a theoretical support for guiding surgeon selection, improving posterior-stabilized prosthetic design and reducing the prosthetic failure.
The surgical installation accuracy of the components in unicompartmental knee arthroplasty (UKA) is an important factor affecting the joint function and the implant life. Taking the ratio of the medial-lateral position of the femoral component relative to the tibial insert (a/A) as a parameter, and considering nine installation conditions of the femoral component, this study established the musculoskeletal multibody dynamics models of UKA to simulate the patients’ walking gait, and investigated the influences of the medial-lateral installation positions of the femoral component in UKA on the contact force, joint motion and ligament force of the knee joint. The results showed that, with the increase of a/A ratio, the medial contact force of the UKA implant was decreased and the lateral contact force of the cartilage was increased; the varus rotation, external rotation and posterior translation of the knee joint were increased; and the anterior cruciate ligament force, posterior cruciate ligament force and medial collateral ligament force were decreased. The medial-lateral installation positions of the femoral component in UKA had little effect on knee flexion-extension movement and lateral collateral ligament force. When the a/A ratio was less than or equalled to 0.375, the femoral component collided with the tibia. In order to prevent the overload on the medial implant and lateral cartilage, the excessive ligament force, and the collision between the femoral component and the tibia, it is suggested that the a/A ratio should be controlled within the range of 0.427−0.688 when the femoral component is installed in UKA. This study provides a reference for the accurate installation of the femoral component in UKA.
Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.